Contradictions and Opportunities: Reconciling Professional Identity Formation and Competency-Based Medical Education
Bibliographic record
Abstract
The widespread adoption of Competency-Based Medical Education (CBME) has resulted in a more explicit focus on learners' abilities to effectively demonstrate achievement of the competencies required for safe and unsupervised practice. While CBME implementation has yielded many benefits, by focusing explicitly on what learners are doing, curricula may be unintentionally overlooking who learners are becoming (i.e., the formation of their professional identities). Integrating professional identity formation (PIF) into curricula has the potential to positively influence professionalism, well-being, and inclusivity; however, issues related to the definition, assessment, and operationalization of PIF have made it difficult to embed this curricular imperative into CBME. This paper aims to outline a path towards the reconciliation of PIF and CBME to better support the development of physicians that are best suited to meet the needs of society. To begin to reconcile CBME and PIF, this paper defines three contradictions that must and can be resolved, namely: (1) CBME attends to behavioral outcomes whereas PIF attends to developmental processes; (2) CBME emphasizes standardization whereas PIF emphasizes individualization; (3) CBME organizes assessment around observed competence whereas the assessment of PIF is inherently more holistic. Subsequently, the authors identify curricular opportunities to address these contradictions, such as incorporating process-based outcomes into curricula, recognizing the individualized and contextualized nature of competence, and incorporating guided self-assessment into coaching and mentorship programs. In addition, the authors highlight future research directions related to each contradiction with the goal of reconciling 'doing' and 'being' in medical education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.099 |
| Scholarly communication | 0.026 | 0.035 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".